2024 Volume 80 Issue 22 Article ID: 23-22007
In current pedestrian traffic volume surveys, pedestrians passing through the surveyed cross section are generally counted manually, which limits the survey days and times. In recent years, there has been an increase in the number of surveys using video images taken by video cameras, but personal information and privacy must be taken into consideration. Therefore, LiDAR, which can measure the target pedestrian as a set of three-dimensional coordinate points, has been attracting attention. However, repetitive LiDAR cannot measure the measurement range exhaustively and is difficult to be applied to the survey. In this study, we conducted a pedestrian traffic survey using deep learning with point cloud data measured by non-iterative LiDAR, which can measure the measurement range exhaustively. The results of pedestrian counting showed that the correct response rate was 67.2% in the low case and 84.7% in the high case, indicating that the point cloud data measured by non-repeatable LiDAR has potential to be applied to pedestrian traffic volume surveys.
